Abstract: Computational social science leverages new data sources and computational methods to expand the “sociological imagination”, connecting individual milieus to the wider sociological conditions. Beyond extending the existing approaches, can new computational tools also help expanding the scope of our imagination? In this talk, I will talk about how simple representation learning techniques (esp. “word2vec”) can allow us to think about the text and network data in new ways. In particular, I will discuss how a subtle, less known bias in the word2vec model lets us do better ground the model.

Speaker Bio: Yong-Yeol (YY) Ahn is an Associate Professor at Indiana University School of Informatics, Computing, and Engineering. He was a Visiting Professor at MIT during 2020-2021. Before joining Indiana University, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after earning his PhD in Statistical Physics from KAIST in 2008. His research focuses on developing network science and machine learning methods, and applying them to complex social and biological systems. He is a recipient of several awards including Microsoft Research Faculty Fellowship and LinkedIn Economic Graph Challenge.